Interactive Chan-Vese Approach with Random Walk for Medical Images Segmentation

Mohammadreza Hosseini, Arcot Sowmya, Tomasz Bednarz

Abstract

In this paper, we present a novel interactive variational approach to image segmentation within a Chan-Vese framework. We propose a parameterized energy function that can be modified based on user input, and also incorporate in it a probabilistic term that defines reachability of a pixel from a user-selected `internal’ object pixel. The proposed approach shows promising improvement over automatic segmentation methods when applied to medical images.

References

  1. Ben-Zadok, N., Riklin-Raviv, T., Kiryati, N., 2009. Interactive level set segmentation for image-guided therapy. In IEEE Int. Symp. on Biomedical Imaging, pages 1079-1082.
  2. Cremers, D., Fluck, O., Rousson, M., Aharon, S., 2007. A probabilistic level set formulation for interactive organ segmentation. In Medical Imaging 2007: Image Processing, 6512 (1): 120-129.
  3. Cremers, D., Fluck, O., Rousson, M., Aharon, S., 2007. A probabilistic level set formulation for interactive organ segmentation. In Medical Imaging 2007: Image Processing, 6512 (1): 120-129. Chan, T., Vese, L., 2001. Active contours without edges. In IEEE Trans. Imag. Proc., vol. 10, pp. 266-277.
  4. Grady, L., 2006. Random walks for image segmentation. In IEEE Trans. Pattern Analysis andMachine Intelligence.
  5. Jha, S.K, Bannerjeeb, P., Banika, S., 2013. Random Walks based Image Segmentation Using Color Space Graphs, In Procedia Technology,Vol. 10, pp. 271-278.
  6. Boykov, Y., Jolly, P., 2001. Interactive Graph Cuts for Optimal Boundary &, Region Segmentation of Objects in N-D Images. In Proc. Int',l Conf. Computer Vision, vol. I, pp. 105-112.
  7. Rother, C., Kolmogorov, V., and Blake, A. 2004. Grabcut-interactive foreground extraction using iterated graph cuts. In ACM Transactions on Graphics (SIGGRAPH).
  8. Freedman, D., Zhang, T., 2005. Interactive Graph Cut Based Segmentation with Shape Priors. In Proc. IEEE Conf. Computer Vision and Pattern Recognition.
  9. Malladi, R., Sethian, J.A, Vemuri, B.C, 1995. Shape modeling with front propagation: A level set approach. In IEEE Trans. Pattern Anal. Machine Intell., vol. 17, pp.158 -175.
  10. Kass, M., Witkin, A.,Terzopoulos, D., 1988. Snakes: Active contour models. In Int. J. Comput. Vis., vol. 1, pp.321 -331 1988.
  11. Cremers, D., Rousson, M., Deriche R., 2007. A review of statistical approaches to level set segmentation: Integrating color, texture, motion, and shape. In Int. J. Comput. Vis., vol. 72, no. 2, pp.195-215.
  12. Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, E., Willsky, A., 2003. A shape based approach to curve evolution for segmentation of medical imagery. In IEEE Trans. Medical Imaging, 22(2).
  13. Chang, H., Yang, Q., Parvin, B., 2008. A Bayesian Approach for Image Segmentation with Shape Priors. In IEEE Conference on Computer Vision and Pattern Recognition.
  14. Boykov, Y., Kolmogorov, V., 2000. Interactive organ segmentation using graph cuts. In Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, pages 276-286.
  15. Zhu, Y., Cheng, S., Goel, A., 2010. Interactive segmentation of medical images using belief propagation with level sets. In Proc. 2010 IEEEInt. Conf. Image Process., pp.4113-4116.
  16. Ruiz, E., Kjer, H.M, Vera, S., Ceresa, M., Paulsen, P., González-Ballester, M.A., 2015. Random Walks with Shape Prior for Cochlea Segmentation. In Proceedings of CARS 2015.
  17. Grady, L., 2005. Multilabel random walker segmentation using prior models. In IEEE Conference of Computer Vision and Pattern Recognition, San Diego, CA, June 2005, vol. 1, pp. 763-770.
  18. Osher, S., Sethian, J.A., 1988. Fronts propagation with curvature-dependent speed: Algorithms based on Hamilton, Jacobi Formulation. In Jounrnal of Computaional Physics. 12-49.
  19. Xu, C., Prince, J.L., 1988. Snakes,shapes and gradient vector flow. In IEEE image processing. 359-369.
  20. Olszewska, J., De Vleeschouwer, C., Macq, B., 2007. Speedup gradient vector flow b-spline active contours for robust and real-time tracking. In ICASSP, 905-908.
  21. Zhao, F., Xie, X. 2013. An overview of Interactive Medical Image Segmentation. In British Machine Vision Association and Society for Pattern Recognition. 1-22.
Download


Paper Citation


in Harvard Style

Hosseini M., Sowmya A. and Bednarz T. (2016). Interactive Chan-Vese Approach with Random Walk for Medical Images Segmentation . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 63-70. DOI: 10.5220/0005685400630070


in Bibtex Style

@conference{bioimaging16,
author={Mohammadreza Hosseini and Arcot Sowmya and Tomasz Bednarz},
title={Interactive Chan-Vese Approach with Random Walk for Medical Images Segmentation},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016)},
year={2016},
pages={63-70},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005685400630070},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016)
TI - Interactive Chan-Vese Approach with Random Walk for Medical Images Segmentation
SN - 978-989-758-170-0
AU - Hosseini M.
AU - Sowmya A.
AU - Bednarz T.
PY - 2016
SP - 63
EP - 70
DO - 10.5220/0005685400630070